What is some good advice for college students who have no idea what they want to do in life? originally appeared on Quora - the place to gain and share knowledge, empowering people to learn from others and better understand the world.

Answer by Phineas Barnes, SneakerheadVC for First Round Capital, ten years operating in the start up world, on Quora:

Optimize for learning. Always.

I think that too many people try to imagine what they want life to be like in ten years - the job that will make them happy, the geography that will make them comfortable etc. - and then build a map to get there. These maps are built with today's data and if you follow them, you never get what you want because you will be a different person by the time you navigate to the destination on the map (unless you are a doctor, then you will be in your third year of residency).

Alternatively, if you optimize for learning, you will pursue the thing that is most interesting and that can support your needs today. You will measure risk based on your reality today and you will be much more likely to discover the opportunity to generate asymmetric return on your risks - where you take a small risk in your life today and it pays off in ways that fundamentally change the trajectory of your career for the better. I did this when I went to Haverford instead of Harvard or Stanford, when I joined AND 1 instead of going to Wall Street, when I started a company instead of continuing to build AND 1, when I went to business school instead of getting a job or jumping into another company, when I joined First Round instead of joining McKinsey and when I made decisions at First Round to move to New York from Philly and then to San Francisco from New York.

In each case I have optimized to maintain the steepest learning curve I could and while it is not always the easy choice or the one that makes the most sense to the people you love and trust, I have found it to be the best single metric to guide decisions on "what to do in life."

I think a big piece of this is the structural advantage you get when you choose to optimize for learning - learning is about taking in new information and there are two ways to find lots of new information. First is a new subject or area that you know nothing about. This tends to generate a steep learning curve early in the process but as you grow and attain the knowledge and expertise you need to achieve competence leading to excellence, the curve flattens out if the institution is static. The other way to find high density new information is to work in places that are extremely dynamic - and where the rate of change of the institution is the primary driver of the slope of the learning curve. These tend to be found in research areas pushing at the edge of human knowledge or in startups. In each case you have the opportunity to tap into a learning curve that has the potential to remain steep forever - and benefit from the compounding growth opportunities that this creates.

Here is an example: when I joined AND 1, I started as an intern in marketing and the company was early but a brand building machine. It felt great to be part of the core of the company and to be at the center of the current key driver of success. However, when I joined full time, I joined the footwear department - when it consisted of one founder, me and outsourced developers and designers. It was not core to where were were at the time, but it was core to us achieving our long term goals as a company - if you look at NIKE for example, something like 50% of their revenue and 70% of profit is driven by footwear - so I had the opportunity to grow (from an IC into a management role) within the team that was growing within the group (Product development) that was growing within the division (product and marketing) that was growing within the company (AND 1) that was growing within the category (basketball footwear and apparel) that was growing within the market (athletic gear) that was growing overall.

This compounding growth impacted me personally and helped keep my learning curve very, very steep for a long time.

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